summaryrefslogtreecommitdiff
path: root/text_recognizer/networks
diff options
context:
space:
mode:
Diffstat (limited to 'text_recognizer/networks')
-rw-r--r--text_recognizer/networks/__init__.py2
-rw-r--r--text_recognizer/networks/conv_transformer.py49
-rw-r--r--text_recognizer/networks/convnext/__init__.py7
-rw-r--r--text_recognizer/networks/convnext/attention.py79
-rw-r--r--text_recognizer/networks/convnext/convnext.py77
-rw-r--r--text_recognizer/networks/convnext/downsample.py21
-rw-r--r--text_recognizer/networks/convnext/norm.py18
-rw-r--r--text_recognizer/networks/convnext/residual.py16
-rw-r--r--text_recognizer/networks/image_encoder.py45
-rw-r--r--text_recognizer/networks/text_decoder.py55
-rw-r--r--text_recognizer/networks/transformer/__init__.py6
-rw-r--r--text_recognizer/networks/transformer/attention.py109
-rw-r--r--text_recognizer/networks/transformer/decoder.py41
-rw-r--r--text_recognizer/networks/transformer/decoder_block.py44
-rw-r--r--text_recognizer/networks/transformer/embeddings/__init__.py1
-rw-r--r--text_recognizer/networks/transformer/embeddings/axial.py104
-rw-r--r--text_recognizer/networks/transformer/embeddings/rotary.py67
-rw-r--r--text_recognizer/networks/transformer/ff.py45
-rw-r--r--text_recognizer/networks/transformer/norm.py51
19 files changed, 0 insertions, 837 deletions
diff --git a/text_recognizer/networks/__init__.py b/text_recognizer/networks/__init__.py
deleted file mode 100644
index f921882..0000000
--- a/text_recognizer/networks/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-"""Network modules"""
-from text_recognizer.networks.conv_transformer import ConvTransformer
diff --git a/text_recognizer/networks/conv_transformer.py b/text_recognizer/networks/conv_transformer.py
deleted file mode 100644
index d36162a..0000000
--- a/text_recognizer/networks/conv_transformer.py
+++ /dev/null
@@ -1,49 +0,0 @@
-"""Base network module."""
-from typing import Type
-
-from torch import Tensor, nn
-
-from text_recognizer.networks.transformer.decoder import Decoder
-
-
-class ConvTransformer(nn.Module):
- """Base transformer network."""
-
- def __init__(
- self,
- encoder: Type[nn.Module],
- decoder: Decoder,
- ) -> None:
- super().__init__()
- self.encoder = encoder
- self.decoder = decoder
-
- def encode(self, img: Tensor) -> Tensor:
- """Encodes images to latent representation."""
- return self.encoder(img)
-
- def decode(self, tokens: Tensor, img_features: Tensor) -> Tensor:
- """Decodes latent images embedding into characters."""
- return self.decoder(tokens, img_features)
-
- def forward(self, img: Tensor, tokens: Tensor) -> Tensor:
- """Encodes images into token logtis.
-
- Args:
- img (Tensor): Input image(s).
- tokens (Tensor): token embeddings.
-
- Shapes:
- - img: :math: `(B, 1, H, W)`
- - tokens: :math: `(B, Sy)`
- - logits: :math: `(B, Sy, C)`
-
- where B is the batch size, H is the image height, W is the image
- width, Sy the output length, and C is the number of classes.
-
- Returns:
- Tensor: Sequence of logits.
- """
- img_features = self.encode(img)
- logits = self.decode(tokens, img_features)
- return logits
diff --git a/text_recognizer/networks/convnext/__init__.py b/text_recognizer/networks/convnext/__init__.py
deleted file mode 100644
index faebe6f..0000000
--- a/text_recognizer/networks/convnext/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-"""Convnext module."""
-from text_recognizer.networks.convnext.attention import (
- Attention,
- FeedForward,
- TransformerBlock,
-)
-from text_recognizer.networks.convnext.convnext import ConvNext
diff --git a/text_recognizer/networks/convnext/attention.py b/text_recognizer/networks/convnext/attention.py
deleted file mode 100644
index 1334feb..0000000
--- a/text_recognizer/networks/convnext/attention.py
+++ /dev/null
@@ -1,79 +0,0 @@
-"""Convolution self attention block."""
-
-import torch.nn.functional as F
-from einops import rearrange
-from torch import Tensor, einsum, nn
-
-from text_recognizer.networks.convnext.norm import LayerNorm
-from text_recognizer.networks.convnext.residual import Residual
-
-
-def l2norm(t: Tensor) -> Tensor:
- return F.normalize(t, dim=-1)
-
-
-class FeedForward(nn.Module):
- def __init__(self, dim: int, mult: int = 4) -> None:
- super().__init__()
- inner_dim = int(dim * mult)
- self.fn = Residual(
- nn.Sequential(
- LayerNorm(dim),
- nn.Conv2d(dim, inner_dim, 1, bias=False),
- nn.GELU(),
- LayerNorm(inner_dim),
- nn.Conv2d(inner_dim, dim, 1, bias=False),
- )
- )
-
- def forward(self, x: Tensor) -> Tensor:
- return self.fn(x)
-
-
-class Attention(nn.Module):
- def __init__(
- self, dim: int, heads: int = 4, dim_head: int = 64, scale: int = 8
- ) -> None:
- super().__init__()
- self.scale = scale
- self.heads = heads
- inner_dim = heads * dim_head
- self.norm = LayerNorm(dim)
-
- self.to_qkv = nn.Conv2d(dim, inner_dim * 3, 1, bias=False)
- self.to_out = nn.Conv2d(inner_dim, dim, 1, bias=False)
-
- def forward(self, x: Tensor) -> Tensor:
- h, w = x.shape[-2:]
-
- residual = x.clone()
-
- x = self.norm(x)
-
- q, k, v = self.to_qkv(x).chunk(3, dim=1)
- q, k, v = map(
- lambda t: rearrange(t, "b (h c) ... -> b h (...) c", h=self.heads),
- (q, k, v),
- )
-
- q, k = map(l2norm, (q, k))
-
- sim = einsum("b h i d, b h j d -> b h i j", q, k) * self.scale
- attn = sim.softmax(dim=-1)
-
- out = einsum("b h i j, b h j d -> b h i d", attn, v)
-
- out = rearrange(out, "b h (x y) d -> b (h d) x y", x=h, y=w)
- return self.to_out(out) + residual
-
-
-class TransformerBlock(nn.Module):
- def __init__(self, attn: Attention, ff: FeedForward) -> None:
- super().__init__()
- self.attn = attn
- self.ff = ff
-
- def forward(self, x: Tensor) -> Tensor:
- x = self.attn(x)
- x = self.ff(x)
- return x
diff --git a/text_recognizer/networks/convnext/convnext.py b/text_recognizer/networks/convnext/convnext.py
deleted file mode 100644
index 9419a15..0000000
--- a/text_recognizer/networks/convnext/convnext.py
+++ /dev/null
@@ -1,77 +0,0 @@
-"""ConvNext module."""
-from typing import Optional, Sequence
-
-from torch import Tensor, nn
-
-from text_recognizer.networks.convnext.attention import TransformerBlock
-from text_recognizer.networks.convnext.downsample import Downsample
-from text_recognizer.networks.convnext.norm import LayerNorm
-
-
-class ConvNextBlock(nn.Module):
- """ConvNext block."""
-
- def __init__(self, dim: int, dim_out: int, mult: int) -> None:
- super().__init__()
- self.ds_conv = nn.Conv2d(
- dim, dim, kernel_size=(7, 7), padding="same", groups=dim
- )
- self.net = nn.Sequential(
- LayerNorm(dim),
- nn.Conv2d(dim, dim_out * mult, kernel_size=(3, 3), padding="same"),
- nn.GELU(),
- nn.Conv2d(dim_out * mult, dim_out, kernel_size=(3, 3), padding="same"),
- )
- self.res_conv = nn.Conv2d(dim, dim_out, 1) if dim != dim_out else nn.Identity()
-
- def forward(self, x: Tensor) -> Tensor:
- h = self.ds_conv(x)
- h = self.net(h)
- return h + self.res_conv(x)
-
-
-class ConvNext(nn.Module):
- def __init__(
- self,
- dim: int = 16,
- dim_mults: Sequence[int] = (2, 4, 8),
- depths: Sequence[int] = (3, 3, 6),
- downsampling_factors: Sequence[Sequence[int]] = ((2, 2), (2, 2), (2, 2)),
- attn: Optional[TransformerBlock] = None,
- ) -> None:
- super().__init__()
- dims = (dim, *map(lambda m: m * dim, dim_mults))
- self.attn = attn if attn is not None else nn.Identity()
- self.out_channels = dims[-1]
- self.stem = nn.Conv2d(1, dims[0], kernel_size=7, padding="same")
- self.layers = nn.ModuleList([])
-
- for i in range(len(dims) - 1):
- dim_in, dim_out = dims[i], dims[i + 1]
- self.layers.append(
- nn.ModuleList(
- [
- ConvNextBlock(dim_in, dim_in, 2),
- nn.ModuleList(
- [ConvNextBlock(dim_in, dim_in, 2) for _ in range(depths[i])]
- ),
- Downsample(dim_in, dim_out, downsampling_factors[i]),
- ]
- )
- )
- self.norm = LayerNorm(dims[-1])
-
- def _init_weights(self, m):
- if isinstance(m, (nn.Conv2d, nn.Linear)):
- nn.init.trunc_normal_(m.weight, std=0.02)
- nn.init.constant_(m.bias, 0)
-
- def forward(self, x: Tensor) -> Tensor:
- x = self.stem(x)
- for init_block, blocks, down in self.layers:
- x = init_block(x)
- for fn in blocks:
- x = fn(x)
- x = down(x)
- x = self.attn(x)
- return self.norm(x)
diff --git a/text_recognizer/networks/convnext/downsample.py b/text_recognizer/networks/convnext/downsample.py
deleted file mode 100644
index a8a0466..0000000
--- a/text_recognizer/networks/convnext/downsample.py
+++ /dev/null
@@ -1,21 +0,0 @@
-"""Convnext downsample module."""
-from typing import Tuple
-
-from einops.layers.torch import Rearrange
-from torch import Tensor, nn
-
-
-class Downsample(nn.Module):
- """Downsamples feature maps by patches."""
-
- def __init__(self, dim: int, dim_out: int, factors: Tuple[int, int]) -> None:
- super().__init__()
- s1, s2 = factors
- self.fn = nn.Sequential(
- Rearrange("b c (h s1) (w s2) -> b (c s1 s2) h w", s1=s1, s2=s2),
- nn.Conv2d(dim * s1 * s2, dim_out, 1),
- )
-
- def forward(self, x: Tensor) -> Tensor:
- """Applies patch function."""
- return self.fn(x)
diff --git a/text_recognizer/networks/convnext/norm.py b/text_recognizer/networks/convnext/norm.py
deleted file mode 100644
index 3355de9..0000000
--- a/text_recognizer/networks/convnext/norm.py
+++ /dev/null
@@ -1,18 +0,0 @@
-"""Layer norm for conv layers."""
-import torch
-from torch import Tensor, nn
-
-
-class LayerNorm(nn.Module):
- """Layer norm for convolutions."""
-
- def __init__(self, dim: int) -> None:
- super().__init__()
- self.gamma = nn.Parameter(torch.ones(1, dim, 1, 1))
-
- def forward(self, x: Tensor) -> Tensor:
- """Applies layer norm."""
- eps = 1e-5 if x.dtype == torch.float32 else 1e-3
- var = torch.var(x, dim=1, unbiased=False, keepdim=True)
- mean = torch.mean(x, dim=1, keepdim=True)
- return (x - mean) / (var + eps).sqrt() * self.gamma
diff --git a/text_recognizer/networks/convnext/residual.py b/text_recognizer/networks/convnext/residual.py
deleted file mode 100644
index dfc2847..0000000
--- a/text_recognizer/networks/convnext/residual.py
+++ /dev/null
@@ -1,16 +0,0 @@
-"""Generic residual layer."""
-from typing import Callable
-
-from torch import Tensor, nn
-
-
-class Residual(nn.Module):
- """Residual layer."""
-
- def __init__(self, fn: Callable) -> None:
- super().__init__()
- self.fn = fn
-
- def forward(self, x: Tensor) -> Tensor:
- """Applies residual fn."""
- return self.fn(x) + x
diff --git a/text_recognizer/networks/image_encoder.py b/text_recognizer/networks/image_encoder.py
deleted file mode 100644
index ab60560..0000000
--- a/text_recognizer/networks/image_encoder.py
+++ /dev/null
@@ -1,45 +0,0 @@
-"""Encodes images to latent embeddings."""
-from typing import Tuple, Type
-
-from torch import Tensor, nn
-
-from text_recognizer.networks.transformer.embeddings.axial import (
- AxialPositionalEmbeddingImage,
-)
-
-
-class ImageEncoder(nn.Module):
- """Encodes images to latent embeddings."""
-
- def __init__(
- self,
- encoder: Type[nn.Module],
- pixel_embedding: AxialPositionalEmbeddingImage,
- ) -> None:
- super().__init__()
- self.encoder = encoder
- self.pixel_embedding = pixel_embedding
-
- def forward(self, img: Tensor) -> Tensor:
- """Encodes an image into a latent feature vector.
-
- Args:
- img (Tensor): Image tensor.
-
- Shape:
- - x: :math: `(B, C, H, W)`
- - z: :math: `(B, Sx, D)`
-
- where Sx is the length of the flattened feature maps projected from
- the encoder. D latent dimension for each pixel in the projected
- feature maps.
-
- Returns:
- Tensor: A Latent embedding of the image.
- """
- z = self.encoder(img)
- z = z + self.pixel_embedding(z)
- z = z.flatten(start_dim=2)
- # Permute tensor from [B, E, Ho * Wo] to [B, Sx, E]
- z = z.permute(0, 2, 1)
- return z
diff --git a/text_recognizer/networks/text_decoder.py b/text_recognizer/networks/text_decoder.py
deleted file mode 100644
index 500bcf9..0000000
--- a/text_recognizer/networks/text_decoder.py
+++ /dev/null
@@ -1,55 +0,0 @@
-"""Text decoder."""
-import torch
-from torch import Tensor, nn
-
-from text_recognizer.networks.transformer.decoder import Decoder
-
-
-class TextDecoder(nn.Module):
- """Decodes images to token logits."""
-
- def __init__(
- self,
- dim: int,
- num_classes: int,
- pad_index: Tensor,
- decoder: Decoder,
- ) -> None:
- super().__init__()
- self.dim = dim
- self.num_classes = num_classes
- self.pad_index = pad_index
- self.decoder = decoder
- self.token_embedding = nn.Embedding(
- num_embeddings=self.num_classes, embedding_dim=self.dim
- )
- self.to_logits = nn.Linear(in_features=self.dim, out_features=self.num_classes)
-
- def forward(self, tokens: Tensor, img_features: Tensor) -> Tensor:
- """Decodes latent images embedding into logit tokens.
-
- Args:
- tokens (Tensor): Token indecies.
- img_features (Tensor): Latent images embedding.
-
- Shapes:
- - tokens: :math: `(B, Sy)`
- - img_features: :math: `(B, Sx, D)`
- - logits: :math: `(B, Sy, C)`
-
- where Sy is the length of the output, C is the number of classes
- and D is the hidden dimension.
-
- Returns:
- Tensor: Sequence of logits.
- """
- tokens = tokens.long()
- mask = tokens != self.pad_index
- tokens = self.token_embedding(tokens)
- tokens = self.decoder(x=tokens, context=img_features, mask=mask)
- logits = (
- tokens @ torch.transpose(self.token_embedding.weight.to(tokens.dtype), 0, 1)
- ).float()
- logits = self.to_logits(tokens) # [B, Sy, C]
- logits = logits.permute(0, 2, 1) # [B, C, Sy]
- return logits
diff --git a/text_recognizer/networks/transformer/__init__.py b/text_recognizer/networks/transformer/__init__.py
deleted file mode 100644
index 0d17deb..0000000
--- a/text_recognizer/networks/transformer/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-"""Transformer modules."""
-from text_recognizer.networks.transformer.attention import Attention
-from text_recognizer.networks.transformer.decoder import Decoder, DecoderBlock
-from text_recognizer.networks.transformer.embeddings.rotary import RotaryEmbedding
-from text_recognizer.networks.transformer.ff import FeedForward
-from text_recognizer.networks.transformer.norm import RMSNorm
diff --git a/text_recognizer/networks/transformer/attention.py b/text_recognizer/networks/transformer/attention.py
deleted file mode 100644
index 85f513e..0000000
--- a/text_recognizer/networks/transformer/attention.py
+++ /dev/null
@@ -1,109 +0,0 @@
-"""Implementes the attention module for the transformer."""
-from typing import Optional
-
-import torch
-import torch.nn.functional as F
-from einops import rearrange
-from torch import Tensor, einsum, nn
-
-from text_recognizer.networks.transformer.embeddings.rotary import (
- RotaryEmbedding,
-)
-
-
-class Attention(nn.Module):
- """Standard attention."""
-
- def __init__(
- self,
- dim: int,
- num_heads: int,
- causal: bool = False,
- dim_head: int = 64,
- dropout_rate: float = 0.0,
- ) -> None:
- super().__init__()
- self.dim = dim
- self.scale = self.dim**-0.5
- self.num_heads = num_heads
- self.dim_head = dim_head
-
- self.causal = causal
- self.dropout_rate = dropout_rate
-
- # Single key/value head
- k_dim = dim_head
- v_dim = dim_head
-
- out_dim = self.num_heads * self.dim_head
-
- self.to_q = nn.Linear(self.dim, out_dim, bias=False)
- self.to_k = nn.Linear(self.dim, k_dim, bias=False)
- self.to_v = nn.Linear(self.dim, v_dim, bias=False)
-
- self.dropout = nn.Dropout(p=self.dropout_rate)
-
- # Feedforward
- self.fc = nn.Linear(out_dim, self.dim)
-
- def forward(
- self,
- x: Tensor,
- context: Optional[Tensor] = None,
- mask: Optional[Tensor] = None,
- rotary_embedding: Optional[RotaryEmbedding] = None,
- ) -> Tensor:
- """Computes the attention."""
- b, device = x.shape[0], x.device
-
- q = self.to_q(x)
- q = rearrange(q, "b n (h d) -> b h n d", h=self.num_heads)
- k = self.to_k(context) if context is not None else self.to_k(x)
- v = self.to_v(context) if context is not None else self.to_v(x)
-
- if rotary_embedding is not None:
- q, k, v = map(lambda t: rotary_embedding.rotate(t), (q, k, v))
-
- energy = einsum("b h i d, b j d -> b h i j", q, k) * self.scale
- mask_value = -torch.finfo(energy.dtype).max
- energy = apply_input_mask(b, k, energy, mask, mask_value, device)
- if self.causal:
- energy = apply_causal_mask(energy, mask, mask_value, device)
-
- attn = F.softmax(energy, dim=-1)
- attn = self.dropout(attn)
- out = einsum("b h i j, b j d -> b h i d", attn, v)
- out = rearrange(out, "b h n d -> b n (h d)")
- out = self.fc(out)
- return out
-
-
-def apply_input_mask(
- b: int,
- k: Tensor,
- energy: Tensor,
- mask: Optional[Tensor],
- mask_value: Tensor,
- device: str,
-) -> Tensor:
- """Applies an input mask."""
- if mask is not None:
- k_mask = torch.ones((b, k.shape[-2]), device=device).bool()
- q_mask = rearrange(mask, "b i -> b () i ()")
- k_mask = rearrange(k_mask, "b j -> b () () j")
- input_mask = q_mask * k_mask
-
- energy = energy.masked_fill_(~input_mask, mask_value)
- return energy
-
-
-def apply_causal_mask(
- energy: Tensor, mask: Tensor, mask_value: Tensor, device: str
-) -> Tensor:
- """Applies a causal mask to the energy tensor."""
- i, j = energy.shape[-2:]
- r = torch.arange(i, device=device)
- mask = rearrange(r, "i -> () () i ()") < rearrange(r, "j -> () () () j")
- mask = F.pad(mask, (j - i, 0), value=False)
- energy.masked_fill_(mask, mask_value)
- return energy
diff --git a/text_recognizer/networks/transformer/decoder.py b/text_recognizer/networks/transformer/decoder.py
deleted file mode 100644
index 826bc13..0000000
--- a/text_recognizer/networks/transformer/decoder.py
+++ /dev/null
@@ -1,41 +0,0 @@
-"""Transformer decoder module."""
-from copy import deepcopy
-from typing import Optional
-
-from torch import Tensor, nn
-
-from text_recognizer.networks.transformer.decoder_block import DecoderBlock
-from text_recognizer.networks.transformer.embeddings.rotary import RotaryEmbedding
-
-
-class Decoder(nn.Module):
- """Decoder Network."""
-
- def __init__(
- self,
- depth: int,
- dim: int,
- block: DecoderBlock,
- rotary_embedding: RotaryEmbedding,
- ) -> None:
- super().__init__()
- self.depth = depth
- self.rotary_embedding = rotary_embedding
- self.blocks = nn.ModuleList([deepcopy(block) for _ in range(self.depth)])
- self.ln = nn.LayerNorm(dim)
-
- def forward(
- self,
- x: Tensor,
- context: Optional[Tensor] = None,
- mask: Optional[Tensor] = None,
- ) -> Tensor:
- """Applies attention blocks."""
- for block in self.blocks:
- x = block(
- x=x,
- context=context,
- mask=mask,
- rotary_embedding=self.rotary_embedding,
- )
- return self.ln(x)
diff --git a/text_recognizer/networks/transformer/decoder_block.py b/text_recognizer/networks/transformer/decoder_block.py
deleted file mode 100644
index b8eb5c4..0000000
--- a/text_recognizer/networks/transformer/decoder_block.py
+++ /dev/null
@@ -1,44 +0,0 @@
-"""Transformer decoder module."""
-from copy import deepcopy
-from typing import Optional, Type
-
-from torch import Tensor, nn
-
-from text_recognizer.networks.transformer.attention import Attention
-from text_recognizer.networks.transformer.embeddings.rotary import RotaryEmbedding
-from text_recognizer.networks.transformer.ff import FeedForward
-
-
-class DecoderBlock(nn.Module):
- """Residual decoder block."""
-
- def __init__(
- self,
- self_attn: Attention,
- norm: Type[nn.Module],
- ff: FeedForward,
- cross_attn: Optional[Attention] = None,
- ) -> None:
- super().__init__()
- self.ln_attn = norm
- self.attn = self_attn
- self.ln_cross_attn = deepcopy(norm)
- self.cross_attn = cross_attn
- self.ln_ff = deepcopy(norm)
- self.ff = ff
-
- def forward(
- self,
- x: Tensor,
- rotary_embedding: RotaryEmbedding,
- context: Optional[Tensor] = None,
- mask: Optional[Tensor] = None,
- ) -> Tensor:
- """Applies decoder block on input signals."""
- x = x + self.attn(self.ln_attn(x), mask=mask, rotary_embedding=rotary_embedding)
- x = x + self.cross_attn(
- x=self.ln_cross_attn(x),
- context=context,
- )
- x = x + self.ff(self.ln_ff(x))
- return x
diff --git a/text_recognizer/networks/transformer/embeddings/__init__.py b/text_recognizer/networks/transformer/embeddings/__init__.py
deleted file mode 100644
index bb3f904..0000000
--- a/text_recognizer/networks/transformer/embeddings/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Positional encodings for transformers."""
diff --git a/text_recognizer/networks/transformer/embeddings/axial.py b/text_recognizer/networks/transformer/embeddings/axial.py
deleted file mode 100644
index 9b872a9..0000000
--- a/text_recognizer/networks/transformer/embeddings/axial.py
+++ /dev/null
@@ -1,104 +0,0 @@
-"""Axial attention for multi-dimensional data.
-
-Stolen from:
- https://github.com/lucidrains/axial-attention/blob/
- eff2c10c2e76c735a70a6b995b571213adffbbb7/axial_attention/axial_attention.py#L100
-"""
-from functools import reduce
-from operator import mul
-from typing import Optional, Sequence
-
-import torch
-from torch import Tensor, nn
-
-
-class AxialPositionalEmbedding(nn.Module):
- def __init__(
- self,
- dim: int,
- axial_shape: Sequence[int],
- axial_dims: Optional[Sequence[int]] = None,
- ) -> None:
- super().__init__()
-
- self.dim = dim
- self.shape = axial_shape
- self.max_seq_len = reduce(mul, axial_shape, 1)
-
- self.summed = axial_dims is None
- axial_dims = ((dim,) * len(axial_shape)) if self.summed else axial_dims
-
- assert len(self.shape) == len(
- axial_dims
- ), "number of axial dimensions must equal the number of dimensions in the shape"
- assert (
- self.summed or not self.summed and sum(axial_dims) == dim
- ), f"axial dimensions must sum up to the target dimension {dim}"
-
- self.weights = ParameterList(self, "weights", len(axial_shape))
-
- for ind, (shape, axial_dim) in enumerate(zip(self.shape, axial_dims)):
- ax_shape = [1] * len(self.shape)
- ax_shape[ind] = shape
- ax_shape = (1, *ax_shape, axial_dim)
- ax_emb = nn.Parameter(torch.zeros(ax_shape).normal_(0, 1))
- self.weights.append(ax_emb)
-
- def forward(self, x: Tensor) -> Tensor:
- """Returns axial positional embedding."""
- b, t, _ = x.shape
- assert (
- t <= self.max_seq_len
- ), f"Sequence length ({t}) must be less than the maximum sequence length allowed ({self.max_seq_len})"
- embs = []
-
- for ax_emb in self.weights.to_list():
- axial_dim = ax_emb.shape[-1]
- expand_shape = (b, *self.shape, axial_dim)
- emb = ax_emb.expand(expand_shape).reshape(b, self.max_seq_len, axial_dim)
- embs.append(emb)
-
- pos_emb = sum(embs) if self.summed else torch.cat(embs, dim=-1)
- return pos_emb[:, :t].to(x)
-
-
-# a mock parameter list object until below issue is resolved
-# https://github.com/pytorch/pytorch/issues/36035
-class ParameterList(object):
- def __init__(self, kls, prefix, length):
- self.ind = 0
- self.kls = kls
- self.prefix = prefix
- self.length = length
-
- def _keyname(self, prefix, ind):
- return f"{prefix}_{ind}"
-
- def append(self, x):
- setattr(self.kls, self._keyname(self.prefix, self.ind), x)
- self.ind += 1
-
- def to_list(self):
- return [
- getattr(self.kls, self._keyname(self.prefix, i)) for i in range(self.length)
- ]
-
-
-class AxialPositionalEmbeddingImage(nn.Module):
- def __init__(
- self,
- dim: int,
- axial_shape: Sequence[int],
- axial_dims: Optional[Sequence[int]] = None,
- ) -> None:
- super().__init__()
- axial_dims = (dim // 2, dim // 2) if axial_dims is None else axial_dims
- assert len(axial_shape) == 2, "Axial shape must have 2 dimensions for images"
- self.dim = dim
- self.pos_emb = AxialPositionalEmbedding(dim, axial_shape, axial_dims)
-
- def forward(self, img):
- b, c, h, w = img.shape
- img = img.permute(0, 2, 3, 1).reshape(b, h * w, c)
- pos_emb = self.pos_emb(img)
- return pos_emb.reshape(b, h, w, self.dim).permute(0, 3, 1, 2)
diff --git a/text_recognizer/networks/transformer/embeddings/rotary.py b/text_recognizer/networks/transformer/embeddings/rotary.py
deleted file mode 100644
index ca0a260..0000000
--- a/text_recognizer/networks/transformer/embeddings/rotary.py
+++ /dev/null
@@ -1,67 +0,0 @@
-"""Roatary embedding.
-
-Stolen from lucidrains:
- https://github.com/lucidrains/rotary-embedding-torch
-
-Explanation of roatary:
- https://blog.eleuther.ai/rotary-embeddings/
-"""
-from inspect import isfunction
-
-from einops import rearrange, repeat
-import torch
-from torch import Tensor, nn
-
-
-class RotaryEmbedding(nn.Module):
- """Rotary positional embedding."""
-
- def __init__(self, dim: int) -> None:
- super().__init__()
- inv_freqs = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
- self.register_buffer("inv_freqs", inv_freqs)
- self.cache = {}
-
- def rotate(self, t: Tensor, dim: int = -2) -> Tensor:
- """Rotate vector."""
- device, n = t.device, t.shape[dim]
- freqs = self.forward(lambda: torch.arange(n, device=device), cache_key=n)
- return apply_rotary_emb(t, freqs)
-
- def forward(self, t: Tensor, cache_key: int) -> Tensor:
- """Encodes tensor x with rotary embeddings."""
- if cache_key in self.cache:
- return self.cache[cache_key]
-
- if isfunction(t):
- t = t()
-
- freqs = self.inv_freqs
- freqs = torch.einsum("..., f -> ... f", t.type(freqs.dtype), freqs)
- freqs = repeat(freqs, "... n -> ... (n r)", r=2)
- self.cache[cache_key] = freqs
- return freqs
-
-
-def rotate_half(x: Tensor) -> Tensor:
- x = rearrange(x, "... (d r) -> ... d r", r=2)
- x1, x2 = x.unbind(dim=-1)
- x = torch.stack((-x2, x1), dim=-1)
- return rearrange(x, "... d r -> ... (d r)")
-
-
-def apply_rotary_emb(t: Tensor, freqs: Tensor, start_index: int = 0) -> Tensor:
- freqs = freqs.to(t)
- rot_dim = freqs.shape[-1]
- end_index = start_index + rot_dim
- assert rot_dim <= t.shape[-1], (
- f"feature dimension {t.shape[-1]} is not of sufficient size to rotate"
- f"in all the positions {rot_dim}"
- )
- t_left, t, t_right = (
- t[..., :start_index],
- t[..., start_index:end_index],
- t[..., end_index:],
- )
- t = (t * freqs.cos()) + (rotate_half(t) * freqs.sin())
- return torch.cat((t_left, t, t_right), dim=-1)
diff --git a/text_recognizer/networks/transformer/ff.py b/text_recognizer/networks/transformer/ff.py
deleted file mode 100644
index 3ccf5b5..0000000
--- a/text_recognizer/networks/transformer/ff.py
+++ /dev/null
@@ -1,45 +0,0 @@
-"""Feedforward layer in transformer.
-
-Stolen from lucidrains:
- https://github.com/lucidrains/x-transformers/blob/main/x_transformers/x_transformers.py
-"""
-from typing import Optional
-
-import torch.nn.functional as F
-from torch import Tensor, nn
-
-
-class GEGLU(nn.Module):
- def __init__(self, dim_in: int, dim_out: int) -> None:
- super().__init__()
- self.fc = nn.Linear(dim_in, dim_out * 2)
-
- def forward(self, x: Tensor) -> Tensor:
- x, gate = self.fc(x).chunk(2, dim=-1)
- return x * F.gelu(gate)
-
-
-class FeedForward(nn.Module):
- def __init__(
- self,
- dim: int,
- dim_out: Optional[int] = None,
- expansion_factor: int = 4,
- glu: bool = True,
- dropout_rate: float = 0.0,
- ) -> None:
- super().__init__()
- inner_dim = dim * expansion_factor
- dim_out = dim_out if dim_out is not None else dim
- in_projection = (
- nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU())
- if not glu
- else GEGLU(dim, inner_dim)
- )
-
- self.mlp = nn.Sequential(
- in_projection, nn.Dropout(dropout_rate), nn.Linear(inner_dim, dim_out)
- )
-
- def forward(self, x: Tensor) -> Tensor:
- return self.mlp(x)
diff --git a/text_recognizer/networks/transformer/norm.py b/text_recognizer/networks/transformer/norm.py
deleted file mode 100644
index 1431327..0000000
--- a/text_recognizer/networks/transformer/norm.py
+++ /dev/null
@@ -1,51 +0,0 @@
-"""Normalization layers for transfromers.
-
-Copied from lucidrains:
- https://github.com/lucidrains/x-transformers/blob/main/x_transformers/x_transformers.py
-
-"""
-from typing import Optional, Type
-
-import torch
-from torch import Tensor, nn
-
-
-class RMSNorm(nn.Module):
- """Root mean square layer normalization."""
-
- def __init__(self, dim: int, eps: float = 1e-8) -> None:
- super().__init__()
- self.scale = dim**-0.5
- self.eps = eps
- self.g = nn.Parameter(torch.ones(dim))
-
- def forward(self, x: Tensor) -> Tensor:
- """Applies normalization."""
- norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
- return x / norm.clamp(min=self.eps) * self.g
-
-
-class PreNorm(nn.Module):
- """Applies layer normalization then function."""
-
- def __init__(
- self,
- normalized_shape: int,
- fn: Type[nn.Module],
- context_dim: Optional[int] = None,
- ) -> None:
- super().__init__()
- self.norm = nn.LayerNorm(normalized_shape)
- self.fn = fn
- self.norm_context = (
- nn.LayerNorm(context_dim) if context_dim is not None else None
- )
-
- def forward(self, x: Tensor, **kwargs) -> Tensor:
- """Applies pre norm."""
- x = self.norm(x)
- if self.norm_context is not None:
- context = kwargs["context"]
- normed_context = self.norm_context(context)
- kwargs.update(context=normed_context)
- return self.fn(x, **kwargs)